Abstract is missing.
- Industrial Experience Report on AI-Assisted Coding in Professional Software DevelopmentRudolf Ramler, Michael Moser, Lukas Fischer 0001, Markus Nissl, René Heinzl. 1-7 [doi]
- Gauging Tech Community Acceptance of Rapid Prototyping in Unfamiliar Programming Languages using LLM ChatbotsKrerkkiat Chusap, Chang Liu 0028. 8-13 [doi]
- LLM4TDD: Best Practices for Test Driven Development Using Large Language ModelsSanyogita Piya, Allison Sullivan. 14-21 [doi]
- LLM-based and Retrieval-Augmented Control Code GenerationHeiko Koziolek, Sten Grüner, Rhaban Hark, Virendra Ashiwal, Sofia Linsbauer, Nafise Eskandani. 22-29 [doi]
- Learn to Code Sustainably: An Empirical Study on Green Code GenerationTina Vartziotis, Ippolyti Dellatolas, George Dasoulas, Maximilian Schmidt, Florian Schneider, Tim Hoffmann, Sotirios Kotsopoulos, Michael Keckeisen. 30-37 [doi]
- LLM-based Control Code Generation using Image RecognitionHeiko Koziolek, Anne Koziolek. 38-45 [doi]
- Translation of Low-Resource COBOL to Logically Correct and Readable Java leveraging High-Resource Java RefinementShubham Gandhi, Manasi Patwardhan 0001, Jyotsana Khatri, Lovekesh Vig, Raveendra Kumar Medicherla. 46-53 [doi]
- Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration ToolsShreya Bhatia, Tarushi Gandhi, Dhruv Kumar 0001, Pankaj Jalote. 54-61 [doi]
- PromptSet: A Programmer's Prompting DatasetKaiser Pister, Dhruba Jyoti Paul, Ishan Joshi, Patrick Brophy. 62-69 [doi]
- Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static ContextYichen Li 0003, Yun Peng, Yintong Huo, Michael R. Lyu. 70-74 [doi]
- Evaluating Fault Localization and Program Repair Capabilities of Existing Closed-Source General-Purpose LLMsShengbei Jiang, Jiabao Zhang, Wei Chen, Bo Wang, Jianyi Zhou, Jie Zhang. 75-78 [doi]
- MoonBit: Explore the Design of an AI-Friendly Programming LanguageHaoxiang Fei, Yu Zhang, Hongbo Zhang, Yanlin Wang, Qing Liu. 79-83 [doi]
- Toward a New Era of Rapid Development: Assessing GPT-4-Vision's Capabilities in UML-Based Code GenerationGábor Antal, Richárd Vozár, Rudolf Ferenc. 84-87 [doi]
- Investigating the Proficiency of Large Language Models in Formative Feedback Generation for Student ProgrammersSmitha S. Kumar, Michael Adam Lones, Manuel Maarek, Hind Zantout. 88-93 [doi]
- Tackling Students' Coding Assignments with LLMsAdam Dingle, Martin Krulis. 94-101 [doi]
- Applying Large Language Models to Enhance the Assessment of Parallel Functional Programming AssignmentsSkyler Grandel, Douglas C. Schmidt, Kevin Leach. 102-110 [doi]
- An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering ProjectSanka Rasnayaka, Guanlin Wang, Ridwan Shariffdeen, Ganesh Neelakanta Iyer. 111-118 [doi]
- LLMs for Relational Reasoning: How Far are We?Zhiming Li, Yushi Cao, XiuFeng Xu, Junzhe Jiang, Xu Liu, Yon Shin Teo, Shang-Wei Lin, Yang Liu. 119-126 [doi]
- Semantically Aligned Question and Code Generation for Automated Insight GenerationAnanya Singha, Bhavya Chopra, Anirudh Khatry, Sumit Gulwani, Austin Z. Henley, Vu Le 0002, Chris Parnin, Mukul Singh, Gust Verbruggen. 127-134 [doi]